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We introduce the local composite quantile regression (LCQR) to causalinference in regression discontinuity (RD) designs. Kai et al. (2010) study theefficiency property of LCQR, while we show that its nice boundary performancetranslates to accurate estimation of treatment effects in RD under a variety ofdata generating processes. Moreover, we propose a bias-corrected and standarderror-adjusted t-test for inference, which leads to confidence intervals withgood coverage probabilities. A bandwidth selector is also discussed. Forillustration, we conduct a simulation study and revisit a classic example fromLee (2008). A companion R package rdcqr is developed.

Local Composite Quantile Regression for Regression Discontinuity